Automatic verification and authentication of personal identity based on biometric measurements has become popular in security applications. Existing commercial systems exploit a myriad of biometric modalities including voice characteristics, iris scan and finger print. However, as a source of biometric information, the human face plays a particularly important role because facial images (e.g. photographs) can easily be acquired and also they convey discriminatory features which are routinely used for recognition by humans without the need for specialist training. This opens the possibility for a close human-machine interaction and cooperation.
Unfortunately, hitherto, the performance of automatic systems for face verification has often been poor. Although considerable progress has been made over recent years, face verification is still a challenging task. For this reason one of the recent paradigms has been to use multiple modalities to achieve robustness and improved performance. Typically, voice and face data has been combined, as described by S. Ben-Yacoub et al in “Audio-visual person verification”, Computer Vision and Pattern Recognition, pp 580-585, June 1999, IEEE Computer Society to achieve better verification rates (i.e. lower false rejection and false acceptance rates). However, the merits of the combination of other modalities including face profile, lip dynamics and 3D face information to name but a few have also been investigated. Although the multimodal approach has been demonstrated to achieve significant improvements, there is still the need to improve the performance of the constituent biometric subsystems to drive the error rates even lower. Some advances recently reported in this context include those described in “On matching scores for IDA-based face verification” by J. Kittler et al, British Machine Vision Conference 2000, ed M. Mirmehdi and B. Thomas.
As another direction to gain performance improvements, attempts have been made to combine the outputs of several decision making systems. This approach draws on the results obtained from multiple classifier fusion described in “Multiple Classifier Systems” edited by J. Kittler et al, Springer-Verlag, Berlin 2000. By combining several opinions as to authenticity it is possible to reduce the error variance of the outputs of the individual experts and achieve better error rates. In “Face verification using client-specific fisher faces” by J. Kittler et al, The Statistics of Directions, Shapes and Images pages 63-66, 2000, ed. J. T. Kent and R. G. Aykroyd, it was shown that by combining the scores of several diverse face verification systems the error rate of the best expert could be reduced by more than 42%. However, such ad hoc designs of multiple expert systems may not necessarily produce the best solutions.
With a view to at least alleviating the afore-mentioned problems the present invention provides a personal identity verification process and system employing an error correcting output coding (ECOC) approach. ECOC was developed for channel coding. The basic idea of ECOC is to allocate additional bits over and above the number of bits required to code a source message in order to provide error correcting capability. In the context of pattern classification the idea implies that each class of pattern is represented by a more complex code than the conventional code, Zij=0 ∀i≠j and Zij=1 i=j. The implementation of such error resilient code requires more than the usual number of classifiers.
The main difficulty in applying the ECOC classification method to the problem of face verification is that face verification is a two class problem (i.e. involving a client class and an imposter class), whereas ECOC is suited exclusively to multiclass problems. This difficulty can be overcome by adopting a two stage solution to the verification problem. In the first stage, the verification task can be viewed as a recognition problem and an ECOC design can be developed to generate class specific discriminants. In fact, only the discriminant for the class of the claimed identity is needed. In the second stage, the hypothesis that the generated discriminant is consistent with the distributions of responses for the particular client.